Deep learning analysis of breast arterial calcifications: a study on predicting cardiovascular disease in women

Mu'ath Ibrahim; Ziba Gandomkar; Mo'ayyad E. Suleiman; Jessica Yi; Seyedamir Tavakoli Taba; Patrick C. Brennan

doi:10.1117/12.3007042

29 March 2024 Deep learning analysis of breast arterial calcifications: a study on predicting cardiovascular disease in women

Mu'ath Ibrahim, Ziba Gandomkar, Mo'ayyad E. Suleiman, Jessica Yi, Seyedamir Tavakoli Taba, Patrick C. Brennan

Author Affiliations +

Proceedings Volume 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment; 1292905 (2024) https://doi.org/10.1117/12.3007042
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Breast arterial calcifications (BAC) are increasingly recognized as indicative markers for cardiovascular disease (CVD). In this study, we manually annotated BAC areas on 3,330 mammograms, forming the foundational dataset for developing a deep learning model to automate assessment of BAC. Using this annotated data, we propose a semi-supervised deep learning approach to analyze unannotated mammography images, leveraging both labeled and unlabeled data to improve BAC segmentation accuracy. Our approach combines the U-net architecture, a well-established deep learning method for medical image segmentation, with a semi-supervised learning technique. We retrieved mammographic examinations of 6,000 women (3,000 with confirmed CVD and 3,000 without) from the screening archive to allow for a focused study. Utilizing our trained deep learning model, we accurately detected and measured the severity of BAC in these mammograms. Additionally, we examined the time between mammogram screenings and the occurrence of CVD events. Our study indicates that both the presence and severity (grade) of BAC, identified and measured using deep learning for automated segmentation, are crucial for primary CVD prevention. These findings underscore the value of technology in understanding the link between BAC in mammograms and cardiovascular disease, shaping future screening and prevention strategies for women's health.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mu'ath Ibrahim, Ziba Gandomkar, Mo'ayyad E. Suleiman, Jessica Yi, Seyedamir Tavakoli Taba, and Patrick C. Brennan "Deep learning analysis of breast arterial calcifications: a study on predicting cardiovascular disease in women", Proc. SPIE 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 1292905 (29 March 2024); https://doi.org/10.1117/12.3007042

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